In this article, we aim to provide a literature review of different formulations and approaches
to continual reinforcement learning (RL), also known as lifelong or non-stationary RL. We …

This work studies the question of Representation Learning in RL: how can we learn a
compact low-dimensional representation such that on top of the representation we can …

We study model-based offline Reinforcement Learning with general function approximation
without a full coverage assumption on the offline data distribution. We present an algorithm …

We present BRIEE, an algorithm for efficient reinforcement learning in Markov Decision
Processes with block-structured dynamics (ie, Block MDPs), where rich observations are …

The low-rank MDP has emerged as an important model for studying representation learning
and exploration in reinforcement learning. With a known representation, several model-free …

We propose a general framework to design posterior sampling methods for model-based
RL. We show that the proposed algorithms can be analyzed by reducing regret to Hellinger …

We study the problem of representational transfer in RL, where an agent first pretrains in a
number of\emph {source tasks} to discover a shared representation, which is subsequently …

We study representation learning for Offline Reinforcement Learning (RL), focusing on the
important task of Offline Policy Evaluation (OPE). Recent work shows that, in contrast to …

We study the problem of model selection in batch policy optimization: given a fixed, partial-
feedback dataset and M model classes, learn a policy with performance that is competitive …

We consider a contextual bandit problem with $ S $ contexts and $ K $ actions. In each
round $ t= 1, 2,\dots $ the learnerobserves a random context and chooses an action based …